It is conventionally known that an image is blurred when taking a picture with a signal processing device such as camera and the like. A blur of an image is mainly due to hand jiggling (camera shaking) when taking a picture, various aberrations in optics, lens distortion and the like.
In order to stabilize an image, there are two methods, moving lens and electronic processing. As a method of moving a lens, for example, the patent document 1 discloses a method of stabilizing an image by detecting hand jiggling and moving a predetermined lens in response to hand jiggling. As a method of electronic processing, the document 2 discloses a method of producing a restored image by detecting displacement of a camera's optical axis with an angular velocity sensor, gaining a transfer function showing a blurry state when taking a picture from detected angular velocity, and inversely transforming the barycenter value Ga of the point spread transfer function about a shot image.
Further, signals from a sound, an x-ray photograph, a microscopic picture, an earth quake wave form and the like except a general shot image might have been also blurred and deteriorated due to fluctuation and other causes.
[Patent Document 1]
Japanese patent publication 6-317824 A (see the summary)
[Patent Document 2]
Japanese patent publication 11-24122A (see the summary)
A camera, equipped with a method of stabilizing an image shown in the patent document 1, becomes a large size because of necessity of a space for mounting hardware such as a lens such as motor and the like. Further, installing such hardware and a driving circuit needs to increase a cost. On the other hand, a method of stabilizing an image shown in the patent document 2 includes following issues, although the method does not have the above mentioned issue. Namely, following two issues cause difficulty of producing a restored image in actual; though inversed transformation of the barycenter value Ga of the point spread transfer function theoretically produces a restored image.
First, the gained point spread transfer function is extremely weak in handling noises and information errors about a blur and slight variation of them greatly affects the values of the function. This variation results in a disadvantage in that a restored image produced by the inversed transformation is far from a stabilized image without hand jiggling and out of use in actual. Second, the inverse transformation factoring noises sometime needs a method of estimating solutions by using singular value decomposition about solutions in simultaneous equations. But calculated values for such estimation run into astronomical numbers, actually yielding a high risk of remaining unsolved.
These issues regarding an image emerge from general various kinds of signal data. It is difficult to restore a signal by using the inverse transformation if the gained transfer function is inaccurate or even if it is accurate. Further, it is impossible to gain a perfectly accurate transfer function if processing objects in nature.